Instructions to use toolevalxm/MedVision-XRay-TestRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedVision-XRay-TestRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/MedVision-XRay-TestRepo") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("toolevalxm/MedVision-XRay-TestRepo") model = AutoModelForImageClassification.from_pretrained("toolevalxm/MedVision-XRay-TestRepo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from toolevalxm/MedVision-XRay-TestRepo: direct link, hf CLI and curl.
- Browser
- Download file 128 Bytes
-
https://huggingface.co/toolevalxm/MedVision-XRay-TestRepo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://toolevalxm/MedVision-XRay-TestRepo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/toolevalxm/MedVision-XRay-TestRepo/resolve/main/pytorch_model.bin
128 Bytes
- Xet hash:
- 00d2c2078ff3e26ed18a814df511205df429e72b1722b97edd9653f6e2a13304
- Size of remote file:
- 128 Bytes
- SHA256:
- 719d533eaadd5eeaa3271abb6e1f5b81977c514c248736ff3a8105dafdc53277
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